This invention relates to pattern localization and, more particularly, to a system combining two dimensional pattern localization with text recognition to enable indexing text keyword extraction.
Image content-based retrieval is becoming a powerful alternative/addition to conventional text annotation-based retrieval. Even so, it has yet to reach the robustness and computational effectiveness of text-based retrieval. Text-based retrieval, on the other hand, is notoriously lacking in precision, even when boolean combinations of key-words are allowed. It is a common observation with those using popular conventional search that full text indexing of documents (scanned or electronic) causes a large number of irrelevant documents to be retrieved.
A more productive use of text-based querying is when it is combined with image content-based querying. A special case of this occurs when the text strings relevant for indexing documents occur within image structures, such as text in special regions of a news video or text within region fields of a form. Retrieval based on such structured text can yield fewer but more relevant matching documents.
An example of the above-mentioned special case arises in the area of processing engineering drawing documents, a large number of which still exist in paper form. Creating electronic conversion of such documents is an important business for large format scanner makers. As is known, large format scanners can scan engineering drawing documents at a relatively fast rate of 25 sheets/minute, and are quickly giving rise to very large databases (in excess of 100,000 objects) of large-sized drawing images (e.g., 14000xc3x979000 pixels). Currently, indexing of such documents is done manually with skilled keyboard operators, and is considered a highly labor intensive activity constituting a significant cost in the digitizing of scanned images. Manual indexing by a keyboard operator can also be unreliable since the keywords employed by a user may not match the ones attached to the documents during database creation.
In contrast to full-text indexing of pure text documents, automatic full-text indexing using conventional OCR algorithms will not yield useful results for drawing images. Fortunately, useful text information for indexing such drawing images is found in specific image structures called xe2x80x9ctitle blocksxe2x80x9d. Typically, a title block will include information pertinent for indexing a corresponding drawing, such as part number, name of the unit being depicted, date of design, and architect name. Indexing keyword extraction from such image structures requires that the image structures themselves be first identified.
As will appear from the Detailed Description below, the present invention employs some of the principles underlying a solution for a model indexing problem, namely the principles underlying xe2x80x9cGeometric Hashingxe2x80x9d. Referring to articles by Y. Lamdan and H. J. Wolfson (entitled xe2x80x9cGeometric hashing: A general and efficient model-based recognition schemexe2x80x9d, in Proceeding of the International Conference on Computer Vision, pages 238-249, 1988, and xe2x80x9cTransformation invariant indexingxe2x80x9d in Geometric Invariants in Computer Vision, IT Press, pages 334-352, 1992), Geometric Hashing has been used to identify objects in pre-segmented image regions. Another work extending the basic geometric hashing scheme for use with line features includes an article by F.C.D. Tsai entitled xe2x80x9cGeometric hashing with line featuresxe2x80x9d in Pattern Recognition, Vol. 27, No. 3, pages 377-389, 1994. An extensive analysis of the geometric hashing scheme is provided in an article by W. E. L. Grimson and D. Huttenlocher entitled xe2x80x9cOn the sensitivity of geometric hashingxe2x80x9d, in Proceedings International Conference on Computer Vision, pages 334-339, 1990.
Obtaining suitable geometric hash functions has also been explored in an article by G. Bebis, M. Georgiopolous and N. Lobo entitled xe2x80x9cLearning geometric hashing functions for model-based object recognitionxe2x80x9d in Proceedings International Conference on Computer Vision, pages 543-548, 1995, and a discussion of using the concept of xe2x80x9crehashingxe2x80x9d in the context of geometric hashing is provided in an article by I. Rigoustos and R. Hummel xe2x80x9cMassively parallel model matching: Geometric hashing on the connection machinexe2x80x9d in IEEE Computer, pages 33-41, February 1992.
As mentioned above, manual indexing of drawing documents can be time-consuming and thus undesirable. Moreover, pertinent indexing text may be more readily obtainable from two dimensional patterns (e.g., title blocks) embedded in previously stored drawing documents. Identifying these embedded patterns, however, can be difficult since orientation and/or location of the patterns, relative to many other adjacent patterns, is typically unknown. It would be desirable to provide an indexing system that obtains indexing information automatically from a previously stored first set of drawing documents by localizing a query image with respect to the previously stored first set of drawing documents and then extracts corresponding text therefrom for use in storing a second set of drawing documents.
In accordance with the present invention there is provided a document processing system, comprising: a memory; a plurality of model images stored in said memory, the model images being represented by a first information set with the first information set varying as a function of object-based coordinates, at least one of the plurality of model images including a text containing region having index information intended for use in storing one or more document pages; a query image represented as a second set of information varying as a function of object-based coordinates; an image localization module, communicating with said memory, for corresponding the second set of information with a portion of the first set of information to obtain the text containing region; and a text extraction module, communicating with said image localization module, for extracting the index information from the text containing region to facilitate the storing of the one or more document pages.